A Family of Neural Contextual Matrix Factorization Models for Context-Aware Recommendations

Abstract

Recommender systems can produce item recommendations tailored to user preferences and assist user decision making in several real-world applications. Context-aware recommender systems can be built and developed to adapt the recommendations to different contextual situations, since user preferences may vary from contexts to contexts (e.g., time, location, companion, etc.). Recently, the deep learning and neural network techniques have been applied to help build better recommendation models. In this paper, we extend and propose a general neural contextual matrix factorization framework, evaluate and compare a family of these neural contextual matrix factorization models for context-aware recommendations. Particularly, we exploit and analyze the impact on the performance of context-aware recommendations by considering two factors – the component(s) where contexts can be fused into, and the embedding mode utilized to represent context situations.